import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
lowerCamelCase__  =					logging.getLogger(__name__)


def 				lowerCAmelCase__       (			a__ )      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    git.Repo(search_parent_directories=a__ )
  _UpperCamelCase           =    {
      "repo_id": str(a__ ),
      "repo_sha": str(repo.head.object.hexsha ),
      "repo_branch": str(repo.active_branch ),
  }

  with open(os.path.join(a__    ,     "git_log.json" )    ,     "w" ) as f:
    json.dump(a__    ,     a__    ,     indent=4 )


def 				lowerCAmelCase__       (			a__ )      ->Optional[int]:




  '''simple docstring'''

  if params.n_gpu <= 0:
    _UpperCamelCase           =    0
    _UpperCamelCase           =    -1
    _UpperCamelCase           =    True
    _UpperCamelCase           =    False
    return

  assert torch.cuda.is_available()

  logger.info("Initializing GPUs" )
  if params.n_gpu > 1:
    assert params.local_rank != -1

    _UpperCamelCase           =    int(os.environ["WORLD_SIZE"] )
    _UpperCamelCase           =    int(os.environ["N_GPU_NODE"] )
    _UpperCamelCase           =    int(os.environ["RANK"] )

    # number of nodes / node ID
    _UpperCamelCase           =    params.world_size // params.n_gpu_per_node
    _UpperCamelCase           =    params.global_rank // params.n_gpu_per_node
    _UpperCamelCase           =    True

    assert params.n_nodes == int(os.environ["N_NODES"] )
    assert params.node_id == int(os.environ["NODE_RANK"] )

  # local job (single GPU)
  else:
    assert params.local_rank == -1

    _UpperCamelCase           =    1
    _UpperCamelCase           =    0
    _UpperCamelCase           =    0
    _UpperCamelCase           =    0
    _UpperCamelCase           =    1
    _UpperCamelCase           =    1
    _UpperCamelCase           =    False

  # sanity checks
  assert params.n_nodes >= 1
  assert 0 <= params.node_id < params.n_nodes
  assert 0 <= params.local_rank <= params.global_rank < params.world_size
  assert params.world_size == params.n_nodes * params.n_gpu_per_node

  # define whether this is the master process / if we are in multi-node distributed mode
  _UpperCamelCase           =    params.node_id == 0 and params.local_rank == 0
  _UpperCamelCase           =    params.n_nodes > 1

  # summary
  _UpperCamelCase           =    f'--- Global rank: {params.global_rank} - '
  logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
  logger.info(PREFIX + "Node ID        : %i" % params.node_id )
  logger.info(PREFIX + "Local rank     : %i" % params.local_rank )
  logger.info(PREFIX + "World size     : %i" % params.world_size )
  logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node )
  logger.info(PREFIX + "Master         : %s" % str(params.is_master ) )
  logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node ) )
  logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu ) )
  logger.info(PREFIX + "Hostname       : %s" % socket.gethostname() )

  # set GPU device
  torch.cuda.set_device(params.local_rank )

  # initialize multi-GPU
  if params.multi_gpu:
    logger.info("Initializing PyTorch distributed" )
    torch.distributed.init_process_group(
        init_method="env://"    ,     backend="nccl"    ,     )







def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  np.random.seed(args.seed )
  torch.manual_seed(args.seed )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed )





def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->int:




  '''simple docstring'''

  if index == number_of_items:
    return 0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    knapsack(a__    ,     a__    ,     a__    ,     a__    ,     index + 1 )
  if weights[index] <= max_weight:
    _UpperCamelCase           =    values[index] + knapsack(
        a__    ,     a__    ,     a__    ,     max_weight - weights[index]    ,     index + 1 )
  return max(a__    ,     a__ )


if __name__ == "__main__":
   import doctest

   doctest.testmod()




